Description
Are you up to the challenge of developing innovative data-driven software solutions? Do you wonder where AI can be used in product development? Nowadays, there are countless models available through APIs and open-source solutions that can be used without having to train your own model. It’s all there. Commodity AI is possible. How do you get started? Where in your product makes sense to integrate AI? What features are now possible that weren’t achievable or too expensive before?
If this speaks to you, then this training is for you! Let our experienced trainers introduce you to the practical application of Artificial Intelligence and Machine Learning. Learn how to identify and validate AI/ML use cases, and gain an in-depth understanding of the right tools and strategies for successful implementation and deployment. Throughout the workshop, we use the Domain-driven Design methodology, which requires no prior knowledge.
Day 1: In the first practical part, we will learn about Event Storming and the ML Design Canvas. Event Storming is a method of Collaborative Modeling and Knowledge Crunching, a methodology of Domain-Driven Design. It helps domain experts, technical experts, developers and all other team members to develop a common understanding of the business domain. In doing so, we start from use cases examples, and so we can then identify further use cases in a project for innovative AI/ML technologies. Prior Domain-Driven Design knowledge is not necessary.
Day 2: In the second practical part, we formulate concrete ML problems together, based on the use cases we have found. We do this on the ML Design Canvas, which was introduced on the first day. Afterwards, we structure the ML project on the canvas and specify all components for the training and prediction phases. Then we discuss the Data Landscape Canvas to clarify the data availability as last step.
Your Benefits
Overview of the main concepts of Domain-driven Design
Understand how to analyze a domain with Event Storming
Understand how to find AI/ML use cases in your projects, and structure them using the ML Design Canvas
Be able to conduct your own AI/ML Event Storming workshops
Audience
Software architects, developers, data scientists, product owners, who have made their first practical experiences with Machine Learning.
Training Objectives
Find out which problems and use cases are suitable for ML
Learn the knowledge crunching method of event storming used in DDD in a case study and apply it yourself
Define and prioritize problems and opportunities for ML in business areas and projects
Learn how to use the Machine Learning Canvas to structure ML projects
Learn how to use the Data Landscape Canvas
Your Trainers
Christopher Stolle
INNOQ
Sustainable software architecture, DDD and meaningful use of technology to solve business problems
- AI Products with Domain-driven Design
Christopher Stolle is a principal consultant at INNOQ with more than 10 years of experience in the project business. He focuses on the integration and modernization of software systems and digitization strategies. Furthermore, he has extensive project experience in the design and implementation of large distributed software systems. Recently, he has also been focusing a lot on strategic Domain-Driven Design.
Dr. Larysa Visengeriyeva
INNOQ
Machine Learning and MLOps
- AI Products with Domain-driven Design
- Data Mesh: Introduction
Larysa is a senior consultant with INNOQ in Berlin. She received her doctorate in Augmented Data Quality Management at the TU Berlin. At INNOQ she is working on the operationalization of Machine Learning (MLOps). She’s the author of ml-ops.org.
Technical Information and Books
ML-Ops.org
The comprehensive resource on everything MLOps. Created and maintained by Larysa Visengeriyeva. Read more
Fairness and Artificial Intelligence
Classical software testing cannot simply be transferred to AI. Model governance and internal audits are required to ensure fairness. Read more
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